Carbon emission evaluation method for waste incineration power generation

Abstract

The present application belongs to the field of data processing of industrial process supervision, and discloses a carbon emission evaluation method for waste incineration power generation, which comprises the following steps: synchronizing business management data with operation parameters of a process control system, establishing a background thermal power baseline model in a new material-free period, calculating the deviation integral of actual thermal power relative to the baseline model after each feeding, obtaining an instantaneous energy response value, when signal superposition is caused by high-frequency feeding, enabling a demixing processing based on a self-learning response template to separate each response, and periodically injecting standard fuel to calibrate the baseline model. The present application utilizes the discrete characteristics of commercial events to segment and attribute continuous industrial data, and reconstructs the corresponding relationship between material batches of a specific source and their real combustion performance in the furnace in the case that garbage physical forms have been mixed, thereby solving the technical problem of invalid management information caused by homogenization operation in production.

Description

Technical Field

[0001] This invention relates to a carbon emission assessment method for waste incineration power generation, belonging to the field of data processing technology for industrial process monitoring. Background Technology

[0002] Currently, in the operation and management of waste-to-energy plants, distributed control systems or data acquisition and monitoring control systems are commonly used to monitor the process flow of the incinerator boiler system. At the same time, enterprise resource planning systems are used to record commercial information of waste entering the plant. This has become the way to ensure stable production and conduct commercial settlement. In order to ensure stable boiler combustion conditions and obtain continuous and stable steam output, a core operating principle at the operational level is to carry out long-term and thorough mixing of various types of waste in the waste pit to achieve short-term homogenization of the calorific value of the waste entering the furnace.

[0003] However, with the increasing demand for carbon emission management and refined cost accounting, management needs to establish an evaluation system that can directly link waste batches from a single source with their actual combustion quality in order to implement differentiated procurement strategies. At this point, an inherent constraint arises at the operational level: the physical homogenization operation that ensures stable production results in a lack of data relevance at the information level, which is the basis for performance attribution. When waste from different sources is physically mixed before entering the furnace, the attributes of any waste hoisted into the furnace become the average value. This makes it impossible to logically attribute fluctuations in the back-end process control data to any specific waste source batch.

[0004] To address this issue, one approach is to analyze the physical or chemical composition of waste before mixing by adding sampling equipment. However, this method is not only costly when dealing with large-scale, highly heterogeneous waste, but also has inherent limitations in sampling representativeness, making it difficult to meet the requirements of real-time, continuous, and economical online management. Therefore, the problem is not a lack of sensors, but rather an inherent contradiction within the existing technological system: necessary operational processes lead to the failure of management information. Specifically, existing technologies have the following shortcomings: 1. The logical connection of performance attribution is interrupted, i.e., the physical mixing operation in the production process blocks the direct correspondence between front-end business information and back-end process performance information; 2. The evaluation method is lagging and indirect, i.e., the evaluation of feed quality relies heavily on static data such as weighing upon arrival at the plant, failing to reflect its dynamic performance during actual combustion. Therefore, the technical problem to be solved by this invention is how to establish an instantaneous correspondence between a specific batch of waste and the actual combustion performance in the furnace, using only data from the existing process control system and business management system, without increasing additional hardware investment or changing the existing stable production process. Summary of the Invention

[0005] This invention provides a carbon emission assessment method for waste incineration power generation. Its main purpose is to solve the problem of how to reconstruct the correspondence between waste batches that have been separated by the physical mixing process and the actual combustion performance using existing data without changing the existing process or adding new hardware.

[0006] To achieve the above objectives, the present invention provides a carbon emission assessment method for waste incineration power generation, comprising the following steps:

[0007] Step S1: The business management data, which records the batch feeding timestamps and batch weights of waste, is synchronized with the time series data of the operating parameters representing the operating status of the incinerator-boiler system collected by the process control system within seconds.

[0008] Step S2: During the combustion period when no new material is fed, the real-time thermal power is calculated based on the main steam flow rate, main steam pressure and feedwater flow rate in the operating parameters, and a background thermal power baseline model characterizing the stable combustion state of the system is established.

[0009] Step S3: For each batch of waste feed, within a preset time window after the feed timestamp, calculate the deviation of the actual thermal power obtained from the operation parameters relative to the background thermal power baseline model, and integrate the deviation to obtain the instantaneous energy response value characterizing the energy contribution of the waste batch.

[0010] Step S4: When the interval between two consecutive batches of waste feeding is less than a preset threshold, the single feeding response template based on learning from historical data is activated. The actual thermal power deviation caused by the superposition of multiple consecutive feedings is processed by sequence signal dealiasing to separate the instantaneous energy response value belonging to each independent feeding.

[0011] Step S5: Periodically perform baseline calibration. Baseline calibration involves injecting a preset amount of standard reference fuel with known calorific value into the incinerator, and obtaining the measured reference response value using the calculation method in step S3. Then, determine the calibration coefficient based on the measured reference response value and the theoretical calorific value of the standard reference fuel, and use the calibration coefficient to adjust the background thermal power baseline model.

[0012] Preferably, the method also performs the following steps in parallel: when acquiring the operating parameters in step S1, an additional fast response parameter selected from the flue gas temperature at the furnace outlet and the negative pressure in the furnace is acquired. The fast response parameter has a faster response speed than the operating parameter used to calculate the instantaneous energy response value. Within a preset early warning monitoring window after each batch of waste is fed, the time change rate of the fast response parameter is calculated. And when the value of the time change rate exceeds a preset hazard threshold, a potential hazard warning associated with the waste batch source information is generated. The generation of the potential hazard warning is independent of the calculation process of the instantaneous energy response value.

[0013] Preferably, in step S5, the calibration coefficient is defined as the thermal conductivity efficiency factor. The calculation method is as follows: ,in, This is the theoretical reference response value that should be produced by the complete combustion of standard reference fuel. The measured reference response value is used; the adjustment of the background thermal power baseline model is specifically to multiply the entire background thermal power baseline model by the heat conduction efficiency factor.

[0014] Preferably, the establishment of the single feeding response template in step S4 includes: automatically filtering isolated feeding events from historical business management data and operational parameter time series data where the interval between feeding events is greater than a preset time window; extracting time series curves of actual thermal power deviation corresponding to the isolated feeding events without overlapping; and performing time alignment and amplitude normalization on the extracted multiple time series curves, and performing average calculation to construct a single feeding response template that characterizes the standard response characteristics of the incinerator-boiler system.

[0015] Preferably, the dealiasing of the sequence signal in step S4 is implemented using an iterative residual minimization algorithm. The algorithm optimizes the solution of the instantaneous energy response value attributed to each feeding, so that the fitting error between the linear superposition of the single feeding response template of each independent feeding after scaling and time shifting the corresponding instantaneous energy response value and the deviation of the actual heat power formed by the superposition of multiple feedings is minimized.

[0016] Preferably, the method further includes: calculating a combustion characteristic fingerprint that characterizes the combustion dynamics of a waste batch based on the waveform of time series data of the deviation of the actual thermal power relative to the background thermal power baseline model belonging to each independent waste batch; and associating the combustion characteristic fingerprint with the source information of the waste batch to form an evaluation of the combustion stability of batches from different sources.

[0017] Preferably, the calculation of the combustion characteristic fingerprint includes: performing amplitude normalization processing on the time series data of the actual thermal power deviation to eliminate the influence of the total energy magnitude; and calculating the statistical characteristics of the time series data after amplitude normalization processing, wherein the statistical characteristics are selected from the group consisting of at least one of the following: the maximum slope of the rising segment of the curve characterizing the energy release rate, the skewness characterizing the asymmetry of the energy release process, and the kurtosis characterizing the concentration of the energy release process.

[0018] Preferably, the preset time window in step S3 is 3 to 5 minutes; the preset early warning monitoring window is 10 to 30 seconds.

[0019] Preferably, the method further includes: binding the calculated instantaneous energy response value with the batch weight of the feeding; and as the number of feedings accumulates, automatically constructing an average effective calorific value profile based on multiple combustion data, as well as a variance profile of the feeding quality stability.

[0020] Preferably, the method further includes: generating a comprehensive evaluation result of the carbon emission potential of waste from a specific source based on one or more of the instantaneous energy response value, average effective calorific value profile, frequency of occurrence of potential hazard warnings, and combustion characteristic fingerprint, and using the comprehensive evaluation result for hierarchical management.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] 1. Establish a new information association method. Starting from the waste batch feeding timestamp in the business management system, define an analysis window with a clear cause and effect in the continuous boiler operation parameter time series recorded by the process control system. Within this window, by stripping the background thermal power baseline under stable combustion conditions and integrating the actual thermal power deviation caused by the batch feeding, obtain a response value characterizing its instantaneous energy contribution. This process utilizes the discrete characteristics of business events to effectively segment and attribute continuous industrial production data. Even when the physical form of waste has been uniformly mixed and lost its distinguishability, it is still possible to reconstruct the correspondence between material batches from specific sources and their actual combustion performance in the furnace.

[0023] 2. Construct a parallel, complementary monitoring and early warning process. While calculating the instantaneous energy response value by analyzing operational parameters with high thermal inertia, such as main steam parameters, which are suitable for energy integration, the system also monitors the rate of change of parameters with faster response speeds, such as flue gas temperature at the furnace outlet. When a feeding event occurs, if the rate of change of flue gas temperature exceeds the preset hazard threshold within a short period of time, the system immediately generates a potential hazard warning associated with the source of that batch. At this time, the response process of the main steam parameters may not have fully unfolded. This parallel processing of the fast and slow parameter channels expands the evaluation of the feeding batch from a single energy value dimension to a multi-dimensional management of combustion process risks and equipment impacts, enabling cost accounting at the financial level and safety supervision at the production level to be carried out simultaneously.

[0024] 3. This method integrates a dynamic calibration and signal restoration mechanism that ensures the long-term effectiveness of its evaluation benchmark. It periodically injects standard reference fuel into the furnace and calculates the measured reference response value using the same method as for evaluating waste batches. Then, based on the relationship between this response value and the theoretical value, a calibration coefficient reflecting the current heat transfer efficiency of the system is determined to dynamically adjust the background thermal power baseline model. At the same time, when faced with the condition that the thermal response signal superimposed due to the short interval between continuous feeding, the method will activate signal dealiasing processing. That is, based on the single feeding response template learned from historical data, the instantaneous energy response value belonging to each independent feeding is separated from the superimposed signal. The combination of the periodic calibration of the baseline and the real-time restoration of the superimposed signal enables the evaluation results of this method to maintain their objectivity and consistency when facing the challenges of long-term slow decline in equipment performance and short-term rapid changes in production rhythm. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the data processing and decision-making logic of a carbon emission assessment method according to the present invention.

[0026] Figure 2 This is a schematic diagram comparing the effects of dynamic calibration of the thermal power baseline before and after the present invention.

[0027] Figure 3 This is a user role and functional interaction use case diagram for an evaluation system according to the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention provides a carbon emission assessment method for waste-to-energy incineration, applied to the operation and management of waste-to-energy incineration plants. Before waste is fed into the furnace, physical homogenization is typically required. This process disrupts the direct correlation between the commercial information of the waste batch and the actual combustion performance of the waste process. This method, through a series of data processing steps, re-establishes a quantitative correlation between the material batch and its in-furnace combustion performance without altering the existing incineration process. The method includes data synchronization, background thermal power baseline modeling and instantaneous energy response calculation, dealiasing of high-frequency feeding signals, and periodic baseline model calibration. These steps process data scattered across the enterprise resource planning system and process control system into data usable for evaluation.

[0030] In a specific implementation environment, the execution of this method begins with step S1, namely the synchronization of business management data and process control system operating parameters. The time stamps for waste batch feeding recorded by the business management system and the time series data of boiler operating parameters collected by the process control system may differ in time reference. This method uses Network Time Protocol (NTP) to synchronize the time of the server hosting the business management system and the process control system, controlling the error between their system time and the standard time source to within 100 milliseconds. Based on this, the data processing module, with a resolution down to the second, synchronizes the business management data recording waste batch feeding timestamps and batch weights with the time series data representing the incinerator-boiler system operation collected by the process control system. The system aligns the time series data of operational parameters to form a unified data record with the feeding timestamp as the primary key and associated with the corresponding operational parameter sequence, which serves as the input for subsequent attribution calculations. Then, the system executes step S2 to establish a background thermal power baseline model. This step first identifies combustion periods without new feeding through deterministic logic. This logic is defined as follows: if no new batch feeding timestamps are recorded in the business management data within 300 seconds prior to the current time point, the system is determined to be in a stable combustion state. Within the identified stable combustion period, the system calculates the boiler's real-time thermal power based on the real-time collected main steam flow rate, main steam pressure, and feedwater flow rate. In one embodiment, the real-time thermal power... (Unit: megawatt) can be obtained through the formula Calculate, where, Main steam flow rate (unit: kg / s). This refers to the steam enthalpy value at the corresponding main steam pressure (unit: kJ / kg). The system calculates the feedwater enthalpy (in kJ / kg) at the corresponding feedwater temperature. A low-pass filter with a time constant of 180 seconds is applied, and the filtered output is used as the background thermal power baseline model characterizing the steady-state combustion of the system. The model outputs It is used as a dynamic benchmark for calculating subsequent power deviations.

[0031] After obtaining the background thermal power baseline model, the system executes step S3 for each batch of waste feed to calculate the instantaneous energy response value. When the data processing module detects a new feed timestamp... When a preset time window is opened, the length of which is set to 3 to 5 minutes, and in a preferred embodiment, the value is 240 seconds, within which the system calculates the actual heat power. Relative to the background thermal power baseline model deviation ,in, for The actual thermal power at any given time for Background thermal power baseline at time, for Power deviation at time, For time; and to the deviation in Integrating over a time interval yields the instantaneous energy response value characterizing the energy contribution of this waste batch. Its calculation method is as follows ;in, This is the timestamp for the waste batch feeding. For example, if the integral value of the thermal power deviation is 120 megajoules within a 240-second window after a feeding, this 120 megajoules is recorded as the instantaneous energy response value of this feeding and bound to the weight and source information of the batch. In this way, a commercial feeding event is associated with a quantified energy response value. If the interval between two consecutive waste batch feedings is less than a preset time window, the thermal power deviation signal will overlap. At this time, the system activates step S4 to perform dealiasing processing. This processing first requires the establishment of a single feeding response template. The establishment procedure is as follows: automatically filter isolated feeding events with intervals greater than 300 seconds from historical data, extract the corresponding non-aliased thermal power deviation time series curves of these events, and perform time alignment and amplitude normalization processing on at least 50 extracted curves before averaging to construct a single feeding response template. During real-time operation, when the interval between two consecutive feedings is detected to be less than a preset threshold, such as 180 seconds, the system activates dealiasing processing. This processing employs an iterative residual minimization algorithm, the goal of which is to solve for the instantaneous energy response value of each feeding. ,in Number the feeding sequence in the high-frequency feeding sequence, so that the single feeding response template of each independent feeding is formed. After the corresponding instantaneous energy response value Scaling and Time Translation The linear superposition of the results minimizes the fitting error between the observed deviation of the actual thermal power formed by the superposition of multiple consecutive feedings. The application of this algorithm enables the separation of response values ​​belonging to each feeding even under high-frequency production rhythms.

[0032] To address the issue of boiler heat transfer efficiency changing over time due to factors such as coking in furnace tubes, this method also includes step S5, which involves periodically performing baseline calibration. This procedure is set to be performed automatically every 24 hours. During calibration, the system injects a preset amount of standard reference fuel with a known calorific value into the incinerator, for example, 10 kg of diesel fuel with a calorific value of 42.7 MJ / kg. The system uses the same calculation method as in step S3 to obtain the measured reference response value triggered by this standard fuel injection. Simultaneously, the system internally stores the theoretical reference response value corresponding to this standard reference fuel. The system then calculates a calibration factor, i.e., the thermal conductivity factor, based on the megajoules. The calculation method is as follows: For example, if the actual measured values ​​during this calibration... The value is 405.65 megajoules, so the calculation is... Finally, the system will integrate the entire background thermal power baseline model. Multiply the whole by the heat transfer efficiency factor This step allows the evaluation results to be dynamically corrected based on the current physical state of the system; the required main steam enthalpy value is determined when the system performs real-time thermal power calculations. With water enthalpy The data is obtained in real time by the process control system through a built-in digital steam meter function library that conforms to the International Association for the Properties of Water and Steam (IAPWS-IF97) standard. This query takes the real-time pressure and temperature values ​​of the main steam and feedwater collected at a frequency of seconds or higher as input. When sequential signal dealiasing is enabled, the linear superposition model used treats the deviation of the actual thermal power after superposition as the algebraic sum of each independent feeding response template after energy scaling and time shifting. Its engineering applicability is determined by the following calibration procedure: the total weight of homogeneous waste from N consecutive rapid feedings (N≥3) is fed in an isolated manner, and its instantaneous energy response integral value is recorded. Then, the dealiasing algorithm is applied to the N consecutive feeding events to obtain the instantaneous energy response values ​​of each event after separation. If the sum and relative deviation If the percentage is less than 5%, the model is considered valid under the current operating conditions of the system.

[0033] This invention can also execute a risk management monitoring and early warning process in parallel. In step S1, when acquiring operational parameters, this process additionally acquires a fast-response parameter with a faster response speed than the main steam parameters, such as the furnace outlet flue gas temperature. After each batch of waste is fed, the system opens an early warning monitoring window lasting 10 to 30 seconds. Within this window, the system calculates the time change rate of the furnace outlet flue gas temperature. When the value of the rate of change over time exceeds a preset hazard threshold, for example... If the temperature exceeds 15 degrees Celsius per second, the system generates a potential hazard warning associated with that waste batch. This process extends the evaluation of the feed batch to the risk dimension of the combustion process. It should be noted that this method can further calculate a combustion characteristic fingerprint. After obtaining the time series data of heat power deviation attributable to each independent feed, the system performs amplitude normalization and calculates the statistical characteristics of the processed curve, such as the maximum slope of the rising segment, skewness, and kurtosis. These characteristics together constitute the combustion characteristic fingerprint and are associated with the waste batch. As the number of feeds accumulates, the system can automatically construct an average effective calorific value profile and a feed stability variance profile. These profile data, along with the frequency of potential hazard warnings and the combustion characteristic fingerprint, are used to generate a comprehensive evaluation result. This system provides data support for differentiated procurement strategies. The instantaneous energy response value calculated for each independent feeding event is bound to and divided by the batch weight of that feeding event to obtain the average effective calorific value (AFC) characterizing the energy density of that batch of material. This AFC, along with combustion characteristic fingerprint data such as kurtosis and skewness (characterizing the concentration of energy release process) calculated from the thermal power deviation curve waveform, is stored in a database related to the specific batch source. As the number of feeding events accumulates, the system generates a quantitative evaluation of the energy potential and combustion stability of the supplied material based on the statistical distribution of the constructed AFC time series and combustion characteristic fingerprints. This evaluation result directly serves as the input for subsequent waste management, thereby linking front-end commercial procurement decisions with back-end process combustion performance through data analysis.

[0034] Meanwhile, to ensure the applicability and accuracy of this method in different systems, the preset threshold in step S4 can be determined by statistical analysis of the thermal power deviation curves of multiple isolated feeding events used to construct a single feeding response template. Specifically, the time elapsed from the peak value to 10% of the peak value for each curve is calculated, and the 80th percentile of the obtained time sample set is used as the preset threshold of the system, thereby objectively defining the time interval at which the thermal response signals begin to significantly overlap. In addition, in a preferred embodiment, the iterative residual minimization algorithm described in step S4 can be specifically implemented using the Levenberg-Marquardt algorithm (LM algorithm).

[0035] Example 1: In the management of a waste-to-energy incineration plant that is operating at full capacity, the operations department performs long-term mixing of the incoming waste to ensure the stability of the boiler combustion conditions, thereby achieving short-term homogenization of the calorific value of the incoming materials. This results in any batch of waste entering the furnace becoming an average value in terms of physical properties, making it impossible to obtain decision data that can be directly correlated with combustion performance.

[0036] In this application scenario, the total monthly waste supply from waste-generating units A and B is similar to the weight measured upon arrival at the plant. However, operational records show a slight increase in the frequency of fluctuations in the main boiler steam pressure and temperature during waste processing from unit B. Due to a lack of quantitative data, after the deployment of this invention, following the procedures in the specific implementation, the business management data recording feeding timestamps and batch weights, as well as the operational parameters of the process control system characterizing the incinerator-boiler system, were synchronized at the second level, and a dynamic background thermal power baseline model was established. Within one cycle of system operation, when a waste crane grabs a bucket of waste from unit B and puts it into the furnace, the system records the feeding timestamp of this action, and the data after this timestamp is recorded. Within a 240-second preset time window, two data processing flows are executed in parallel. The first flow calculates the actual thermal power based on parameters such as main steam flow and pressure, and integrates it relative to the background thermal power baseline model to obtain the instantaneous energy response value of the batch. The second flow monitors the rate of change of the flue gas temperature at the furnace outlet within a 30-second preset early warning monitoring window after feeding, based on the rapid response parameter. The system continuously processed 100 feedings from B, and the calculation results were integrated into a feed source performance profile containing two key indicators. The profile shows that the average instantaneous energy response value of batch B is 15% lower than that of A, and its potential hazard warning occurrence frequency is 22%, while the frequency of batch A is only 3% under the same feeding frequency.

[0037] The instantaneous energy response value provides a quantitative explanation for insufficient energy contribution from material supply B, while the frequency of potential hazard warnings provides a quantitative indicator of the impact of material supply on equipment operational stability. Management uses this set of data on the source of materials, which includes both energy contribution and combustion risk dimensions. The application of this method eliminates the conflict between the homogenization operation of the operations department and the cost attribution goals of the management department. The physical mixing process in the waste pit continues, but the correspondence between the business information and process performance information that was previously severed by this process is re-established through the processing of system response data. The technical solution to the problem has shifted from how to physically distinguish the mixed waste to how to decode the process performance corresponding to each business event from the continuous system response data stream using discrete business event timestamps.

[0038] Example 2: To verify the effectiveness of the method of the present invention in distinguishing and quantitatively evaluating waste batches with different combustion characteristics, the following experiment was conducted. The purpose of the experiment was to compare the evaluation results of the traditional evaluation method based solely on weight with those of the data processing method of the present invention under controlled conditions. The experiment was conducted on a waste incinerator-boiler system of the same specifications as in the application scenario. The process control system of this system can collect and record operating parameters such as main steam flow rate, main steam pressure, feedwater flow rate, and flue gas temperature at the furnace outlet at a frequency of 1Hz, with measurement accuracies of ±0.5%, ±0.5%, ±0.5%, and ±0.2%, respectively. .

[0039] Three standardized test batches were designed, with the weight of each batch controlled within the range of 1500 kg ± 50 kg to simulate situations where batches from different sources have the same weight but different contents. The specific batch settings are as follows: Test batch A, composed of dry waste paper and waste plastic, simulates a material with a high calorific value and stable combustion, with a theoretical energy of approximately 18500 MJ; Test batch B, composed of a mixture of high-moisture kitchen waste, simulates a material with a low calorific value and stable combustion, with a theoretical energy of approximately 8200 MJ; Test batch C, composed of dry waste paper mixed with a small amount of industrial solvent waste, simulates a material with a high total calorific value but unstable combustion, with a theoretical energy similar to batch A, approximately 18300 MJ. A control group was included in the experiment. The system consisted of a control group and an experimental group. The control group was evaluated based solely on the batch weight recorded in the business management system. The experimental group, however, used the complete method of this invention to calculate the instantaneous energy response value and monitor potential hazard warnings. The experimental process was as follows: When the incinerator was in stable combustion condition, batches A, B, and C were fed 10 times consecutively, with each feeding interval set to be greater than 10 minutes to avoid the superposition of thermal response signals. For the control group, since the weights of each batch were within the error range, there was no significant difference in the evaluation results. For the experimental group, the system calculated the instantaneous energy response value for each feeding event according to the specific implementation procedure and monitored the rate of change of flue gas temperature at the furnace outlet within 30 seconds after feeding. The core data of the experiment were recorded in Table 1.

[0040] Table 1: Comparison of evaluation results for different test batches;

[0041] Test batch type Batch weight (kg) Theoretical Energy (MJ) Control group evaluation Test group - Mean instantaneous energy response (MJ) Test Group - Number of Potential Hazard Warnings (Total 10) A 1521 18500 Indifference 18455 0 B 1488 8200 Indifference 8270 0 C 1505 18300 Indifference 18240 8 .

[0042] Referring to Table 1, the average instantaneous energy response value calculated by the experimental group showed a correlation with the theoretical energy of each batch. The response values ​​of batches A and C were both above 18,200 megajoules, while the response value of batch B was around 8,300 megajoules. This indicates that the calculation of the instantaneous energy response value can reflect the energy contribution of the material. For batches A and C with similar theoretical energies, the evaluation results of the experimental group diverged. Eight out of ten feedings of batch C triggered potential hazard warnings, while batch A did not trigger any warnings. This is because the industrial solvent waste in batch C caused a large amount of heat to be released instantaneously during its combustion process, causing the rate of change of flue gas temperature at the furnace outlet to exceed the preset hazard threshold. This dynamic process cannot be fully characterized by the instantaneous energy response value, which is based on energy integral. The experimental results show that the method of the present invention, by processing energy-related parameters and risk-related parameters in parallel, can generate an evaluation result that includes two dimensions: energy contribution and combustion stability. This result can distinguish material batches with similar weights but different combustion characteristics, thus providing a basis for material combustion performance-based evaluation. The refined feed management decision provides data support that cannot be obtained from weight data alone. It should be noted that, in order to further quantify the differences in the combustion dynamic process between test batch A and test batch C in this embodiment, the method of the present invention also calculates the combustion characteristic fingerprint of the feeding events that did not trigger the potential hazard warning in the two tests. In the calculation, the time series data of the actual thermal power deviation are first normalized, and then the skewness and kurtosis statistical characteristics are calculated. The results show that the combustion characteristic fingerprint of test batch A is skewness close to 0 and kurtosis value is less than 3, while the combustion characteristic fingerprint of test batch C is skewness greater than 1.5 and kurtosis value greater than 5. This data shows that even in the combustion events that do not exceed the hazard threshold, the energy release process of batch C exhibits asymmetry and short-term concentration. By associating the combustion characteristic fingerprint with the batch source information, the management system can classify the stability of the combustion process of different feeds at a finer granularity, thereby identifying those material batches that, although they did not cause severe impact, still have unstable combustion quality.

[0043] Example 3: This example combines Figures 1 to 3 This section describes a carbon emission assessment method for waste-to-energy incineration, such as... Figure 1As shown, the process begins by acquiring continuous time-series data such as main steam flow, pressure, and furnace flue gas temperature from the process control system, and discrete event data including waste batch feeding timestamps and batch weights from business management data. Step S1 synchronizes and aligns the data from these two sources at the second level. Step S2 establishes a background thermal power baseline model characterizing the stable combustion state of the system by identifying periods without new material, serving as a dynamic evaluation benchmark. Step S3 calculates the deviation of the actual thermal power from the baseline model for each feeding event, and quantifies the instantaneous energy contribution of this feeding by integrating the deviation, obtaining the instantaneous energy response value. When the system determines that high-frequency feeding causes the thermal power response signal to superimpose, step S4 is activated. This step is based on historical data... The single-feed response template established through self-learning in the data performs sequence signal dealiasing processing on the superimposed signals to separate the responses belonging to each independent feed. If there is no signal superposition, this step is skipped. At the same time, a parallel potential hazard warning process, independent of energy calculation, identifies combustion process risks by monitoring the rate of change of rapid response parameters such as furnace outlet flue gas temperature. In addition, step S5, as a periodic feedback calibration link, calculates the heat transfer efficiency factor by injecting standard fuel into the furnace and uses it to dynamically adjust the background thermal power baseline model established in step S2. Finally, the method integrates the processing results of each step to generate an average effective calorific value profile, combustion stability and risk warning evaluation, providing decision support for the graded management of feed batches and the optimization of the combustion process.

[0044] like Figure 2 As shown in the figure, the horizontal axis represents time in hours, and the vertical axis represents thermal power in MW. The figure contains three curves: the dashed line marked as the pre-calibration baseline represents a lower constant power reference value used by the system before calibration; the broken line with data points marked as the actual thermal power represents the actual fluctuating thermal power output of the system over 24 hours; and the thick solid line marked as the post-calibration baseline is an average power reference calculated and adjusted based on the measured system heat transfer efficiency after the periodic calibration procedure, which is closer to the actual operating conditions of the current period. By dynamically adjusting the evaluation benchmark from the pre-calibration baseline to the post-calibration baseline, it is ensured that the calculation of the instantaneous energy response value can eliminate systematic errors caused by long-term performance degradation of the equipment, thereby maintaining the objectivity and consistency of the evaluation results.

[0045] like Figure 3As shown in the diagram, two core participant roles are defined: the management and decision-making level and the operations department. The operations department is responsible for performing periodic baseline calibration and receiving potential hazard warnings from the system. The data required for its operation, such as real-time operating parameters, is provided by the process control system. The management and decision-making level mainly uses the core function of this method, namely, generating a comprehensive evaluation. The generation process of this comprehensive evaluation relies on a series of sub-functions, such as calculating instantaneous energy response, analyzing combustion characteristic fingerprints, and constructing feed source profiles. The basic data required for these functions, such as feed event data, is provided by the business management system. This diagram defines the responsibilities of users at different levels and the interaction relationships between the functional modules within the method and external data systems.

[0046] Example 4: This example provides a calibration procedure for determining specific parameters in the evaluation method of the present invention. This procedure is applied to the initial configuration when deploying the method to a specific incinerator-boiler system. The configuration process involves determining two parameters: a preset time window and a hazard threshold. To determine the value of the preset time window, 100 isolated feeding events are first selected from the historical data collected after the system has been running stably. Here, an isolated feeding event is defined as an event in which there are no other feeding records within 600 seconds before and after the feeding timestamp. For each isolated feeding event, the time series curve of the corresponding actual thermal power deviation is extracted, and the energy accumulation integral curve of the deviation curve from the feeding timestamp is calculated. Subsequently, for each energy accumulation integral curve, the time elapsed to reach 95% of the final integral value is calculated and denoted as . After calculating 100 isolated feeding events, a dataset containing 100... A sample set of values, with the 90th percentile of the sample set being 238.4 seconds, is used to set the preset time window for this specific system to 240 seconds.

[0047] To determine the hazard threshold for the rate of change of furnace outlet flue gas temperature, the system's operating data from the past quarter was used. This data included several unstable events marked by operating records that caused fluctuations in furnace operating conditions due to material issues, as well as a large amount of stable operating data over a long period. The data processing program calculated the time-varying rate of change of furnace outlet flue gas temperature from all historical data. The time series was then analyzed; next, these rate-of-change values ​​were divided into two groups, one from the stable operating period and the other from the 5-minute intervals before and after the unstable event; the data from the stable operating group were statistically analyzed. The 99.9 percentile is 12.5. / second, and statistical analysis of the data for unstable event groups showed that over 85% of the events exhibited [the following]. Peak value greater than 15 Based on this statistical analysis, the hazard threshold for this system is set to 15 per second. / second; By executing the above two calibration procedures, the evaluation method applied to this specific incinerator-boiler system has a preset time window and hazard threshold determined by the operating data of the system.

[0048] Example 5: When the evaluation method is applied to an incinerator that lacks isolated feeding events in its historical data due to long-term high feeding frequency, a template calibration procedure is executed before enabling the sequence signal dealiasing processing function. This procedure is performed during a predetermined low-load operation period, during which 20 standard weight batches of waste are fed, and the interval between each feeding is controlled to be more than 600 seconds, thereby generating a set of isolated feeding events without signal aliasing for template construction.

[0049] The system collects the actual thermal power deviation time series curves corresponding to these 20 feeding events, and constructs a single feeding response template for this incineration plant according to the above steps. To verify the applicability of the template, the system performs a test containing three consecutive rapid feedings with an interval of 60 seconds. After applying the newly generated template to perform sequence signal dealiasing processing, the instantaneous energy response values ​​belonging to the three feedings are obtained. , and The sum of them The instantaneous energy response value measured by feeding a single isolated batch of homogeneous waste with a total weight of three feedings is compared with that measured by feeding a single batch of waste in isolation. The deviation between the two is less than 5%. This calibration procedure provides a technical path for the system to obtain a single feeding response template when historical isolated feeding event data is lacking.

[0050] Example 6: This example provides an engineering procedure for establishing the periodic baseline calibration function reference value in the method of the present invention. This procedure is implemented when the evaluation method is first deployed on an incinerator-boiler system, or after the system has completed a planned overhaul aimed at restoring the cleanliness of the heat exchange surfaces. Its purpose is to establish a theoretical reference response value for the system in a clean state for subsequent calculation of the heat transfer efficiency factor. The procedure is started after the incinerator-boiler system has completed the heat exchange surface cleaning treatment. The system is stably operated at 70% of the rated load and maintained at this condition for no less than 2 hours to allow the system thermal parameters to reach equilibrium. After that, the calibration process begins. Standard reference fuel is injected into the furnace 10 times continuously through an auxiliary fuel injection gun. Each injection is 10 kg of diesel fuel, and the interval between two consecutive injection operations is controlled to be no less than 10 minutes so that the system recovers to a stable background thermal power baseline from the response of the previous injection.

[0051] For these 10 independent standard reference fuel injection events, the evaluation method system uses the same calculation method as in step S3 to calculate the measured reference response value triggered by each event, obtaining a sample set containing 10 measurements. The arithmetic mean of these 10 measurements is taken as the theoretical reference response value of the system under this clean state. Its calculation formula is ,in, This represents the sequence number of each independent measurement operation. If the average of 10 measurements is 428.5 megajoules, this value is stored in the system's configuration file. After this procedure is completed, the system obtains a theoretical reference response value based on the statistical averaging of multiple actual measurements under clean conditions in this specific system. This value was subsequently used as the factor for calculating heat transfer efficiency. This benchmark is used in all subsequent periodic baseline calibration procedures.

[0052] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assessing carbon emissions from waste-to-energy incineration, characterized in that, Includes the following steps: Step S1 involves synchronizing business management data, which records the timestamps and weights of waste batches, with time-series data of operational parameters representing the operating status of the incinerator-boiler system collected by the process control system. The method also executes the following steps in parallel: When acquiring operational parameters in step S1, an additional rapid response parameter selected from furnace outlet flue gas temperature and furnace negative pressure is acquired. The rapid response parameter has a faster response speed than the operational parameters used to calculate the instantaneous energy response value. Within a preset early warning monitoring window after each waste batch is fed, the time change rate of the rapid response parameter is calculated. When the value of the time change rate exceeds a preset hazard threshold, an information-related potential hazard warning is generated. The generation of the potential hazard warning is independent of the calculation process of the instantaneous energy response value. Step S2: During the combustion period when no new material is fed, the real-time thermal power is calculated based on the main steam flow rate, main steam pressure and feedwater flow rate in the operating parameters, and a background thermal power baseline model characterizing the stable combustion state of the system is established. Step S3: For each batch of waste feed, within a preset time window after the feed timestamp, calculate the deviation of the actual thermal power obtained from the operation parameters relative to the background thermal power baseline model, and integrate the deviation to obtain the instantaneous energy response value characterizing the energy contribution of the waste batch. Step S4: When the interval between two consecutive batches of waste feeding is less than a preset threshold, the single feeding response template based on learning from historical data is activated. The actual thermal power deviation caused by the superposition of multiple consecutive feedings is processed by sequence signal dealiasing to separate the instantaneous energy response value belonging to each independent feeding. Step S5: Periodically perform baseline calibration. Baseline calibration involves injecting a preset amount of standard reference fuel with known calorific value into the incinerator, and obtaining the measured reference response value using the calculation method in step S3. Then, determine the calibration coefficient based on the measured reference response value and the theoretical calorific value of the standard reference fuel, and use the calibration coefficient to adjust the background thermal power baseline model.

2. The carbon emission assessment method for waste-to-energy incineration according to claim 1, characterized in that, In step S5, the calibration coefficient is defined as the thermal conductivity efficiency factor. The calculation method is as follows: ,in, This is the theoretical reference response value that should be produced by the complete combustion of standard reference fuel. The measured reference response value is used; the adjustment of the background thermal power baseline model is specifically to multiply the entire background thermal power baseline model by the heat conduction efficiency factor.

3. The carbon emission assessment method for waste-to-energy incineration according to claim 1, characterized in that, The establishment of the single feeding response template in step S4 includes: automatically filtering isolated feeding events from historical business management data and operational parameter time series data where the interval between feeding events is greater than a preset time window; extracting the time series curves of actual heat power deviation corresponding to the isolated feeding events without aliasing; and performing time alignment and amplitude normalization on the extracted multiple time series curves, and then performing average calculation.

4. The carbon emission assessment method for waste-to-energy incineration according to claim 1, characterized in that, In step S4, the dealiasing of the sequence signal is achieved using an iterative residual minimization algorithm. The algorithm optimizes the solution to the instantaneous energy response value attributed to each feeding, so that the fitting error between the linear superposition of the single feeding response template of each independent feeding after scaling and time shifting the corresponding instantaneous energy response value and the deviation of the actual heat power formed by the superposition of multiple consecutive feedings is minimized.

5. The carbon emission assessment method for waste-to-energy incineration according to claim 1, characterized in that, The method also includes: calculating a combustion characteristic fingerprint that characterizes the combustion dynamics of a waste batch based on the waveform of the time series data of the deviation of the actual thermal power relative to the background thermal power baseline model belonging to each independent waste batch; and associating the combustion characteristic fingerprint with the source information of the waste batch.

6. The carbon emission assessment method for waste-to-energy incineration according to claim 5, characterized in that, The calculation of combustion characteristic fingerprints includes: normalizing the amplitude of the time series data of actual thermal power deviation; and calculating the statistical characteristics of the time series data after amplitude normalization. The statistical characteristics are selected from a group consisting of at least one of the following: the maximum slope of the rising segment of the curve characterizing the energy release rate, the skewness characterizing the asymmetry of the energy release process, and the kurtosis characterizing the concentration of the energy release process.

7. The carbon emission assessment method for waste-to-energy incineration according to claim 1, characterized in that, The preset time window in step S3 is 3 to 5 minutes; the preset early warning monitoring window is 10 to 30 seconds.

8. The carbon emission assessment method for waste-to-energy incineration according to claim 1, characterized in that, The method also includes: binding the calculated instantaneous energy response value with the batch weight of the feeding; and automatically constructing an average effective calorific value profile based on multiple combustion data as the number of feedings accumulates.

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